{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.846995Z",
     "start_time": "2020-11-17T09:54:24.825191Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.855397Z",
     "start_time": "2020-11-17T09:54:24.849523Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compare between cell line prediction and cell type-specific prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.864757Z",
     "start_time": "2020-11-17T09:54:24.859443Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.874281Z",
     "start_time": "2020-11-17T09:54:24.868093Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.881488Z",
     "start_time": "2020-11-17T09:54:24.876407Z"
    }
   },
   "outputs": [],
   "source": [
    "# log2_median_ic50, log2_median_ic50_9f, log2_median_ic50_hn, log2_median_ic50_9f_hn, log2_median_ic50_3f_hn, log2_max_conc\n",
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'hn_drug_cw_dw10_100000_model'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.888281Z",
     "start_time": "2020-11-17T09:54:24.883764Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/cell_TPM/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:24.937133Z",
     "start_time": "2020-11-17T09:54:24.891512Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.185150Z",
     "start_time": "2020-11-17T09:54:24.944242Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.196113Z",
     "start_time": "2020-11-17T09:54:25.191586Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Not combined replicate #####\n",
    "\n",
    "# validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "# cv_single_df_list = []\n",
    "# cv_combi_df_list = []\n",
    "\n",
    "# for validation_fname in validation_fname_list:\n",
    "\n",
    "#     cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "#     cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_single_df = cv_single_df.reset_index()\n",
    "\n",
    "#     cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "#     cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_combi_df = cv_combi_df.reset_index()\n",
    "    \n",
    "#     cv_single_df_list += [cv_single_df]\n",
    "#     cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "# cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "# cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.209984Z",
     "start_time": "2020-11-17T09:54:25.202494Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103']\n",
    "\n",
    "# single_drug_id_list = [1007, 133, 1010, 182, 301, 302]\n",
    "# single_drug_list = ['Docetaxel', 'Doxorubicin', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Gefitinib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.228595Z",
     "start_time": "2020-11-17T09:54:25.216095Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.237935Z",
     "start_time": "2020-11-17T09:54:25.234166Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preprocess predicted and observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.260198Z",
     "start_time": "2020-11-17T09:54:25.243960Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.288003Z",
     "start_time": "2020-11-17T09:54:25.265952Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>43.02</td>\n",
       "      <td>35.26</td>\n",
       "      <td>59.48</td>\n",
       "      <td>73.23</td>\n",
       "      <td>2.59</td>\n",
       "      <td>32.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.90</td>\n",
       "      <td>35.80</td>\n",
       "      <td>61.70</td>\n",
       "      <td>74.58</td>\n",
       "      <td>3.10</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.91</td>\n",
       "      <td>40.48</td>\n",
       "      <td>64.77</td>\n",
       "      <td>75.88</td>\n",
       "      <td>6.14</td>\n",
       "      <td>33.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>18.48</td>\n",
       "      <td>28.83</td>\n",
       "      <td>15.77</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.31</td>\n",
       "      <td>32.16</td>\n",
       "      <td>21.91</td>\n",
       "      <td>18.72</td>\n",
       "      <td>13.78</td>\n",
       "      <td>29.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.69</td>\n",
       "      <td>38.61</td>\n",
       "      <td>24.50</td>\n",
       "      <td>24.83</td>\n",
       "      <td>14.27</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>33.32</td>\n",
       "      <td>42.17</td>\n",
       "      <td>23.79</td>\n",
       "      <td>15.58</td>\n",
       "      <td>14.64</td>\n",
       "      <td>45.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.72</td>\n",
       "      <td>46.45</td>\n",
       "      <td>25.15</td>\n",
       "      <td>22.23</td>\n",
       "      <td>13.93</td>\n",
       "      <td>41.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.04</td>\n",
       "      <td>52.38</td>\n",
       "      <td>31.03</td>\n",
       "      <td>25.80</td>\n",
       "      <td>17.89</td>\n",
       "      <td>45.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>50.24</td>\n",
       "      <td>60.62</td>\n",
       "      <td>30.33</td>\n",
       "      <td>31.60</td>\n",
       "      <td>28.97</td>\n",
       "      <td>55.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          43.02  35.26   \n",
       "                                                   2          41.90  35.80   \n",
       "                                                   3          46.91  40.48   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          18.48  28.83   \n",
       "                                                   2          19.31  32.16   \n",
       "                                                   3          23.69  38.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          33.32  42.17   \n",
       "                                                   2          35.72  46.45   \n",
       "                                                   3          39.04  52.38   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          50.24  60.62   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          59.48  73.23   \n",
       "                                                   2          61.70  74.58   \n",
       "                                                   3          64.77  75.88   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          15.77  13.81   \n",
       "                                                   2          21.91  18.72   \n",
       "                                                   3          24.50  24.83   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          23.79  15.58   \n",
       "                                                   2          25.15  22.23   \n",
       "                                                   3          31.03  25.80   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          30.33  31.60   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           2.59  32.91  \n",
       "                                                   2           3.10  31.37  \n",
       "                                                   3           6.14  33.48  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          12.00  32.03  \n",
       "                                                   2          13.78  29.41  \n",
       "                                                   3          14.27  28.43  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          14.64  45.80  \n",
       "                                                   2          13.93  41.51  \n",
       "                                                   3          17.89  45.25  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          28.97  55.86  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.321850Z",
     "start_time": "2020-11-17T09:54:25.293373Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.342933Z",
     "start_time": "2020-11-17T09:54:25.327833Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>delta</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>3.23</td>\n",
       "      <td>10.90</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>2.40</td>\n",
       "      <td>16.56</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1012</td>\n",
       "      <td>0.29</td>\n",
       "      <td>45.18</td>\n",
       "      <td>Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>59.13</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>0.25</td>\n",
       "      <td>46.37</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient  drug_id  delta   kill           drug_name\n",
       "4    HN120     1007   3.23  10.90           Docetaxel\n",
       "5    HN120     1010   2.40  16.56           Gefitinib\n",
       "6    HN120     1012   0.29  45.18          Vorinostat\n",
       "24   HN120      133  -0.61  59.13         Doxorubicin\n",
       "44   HN120      182   0.25  46.37  Obatoclax Mesylate"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.376258Z",
     "start_time": "2020-11-17T09:54:25.348865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>kill_C</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>10.90</td>\n",
       "      <td>39.49</td>\n",
       "      <td>10.90</td>\n",
       "      <td>39.49</td>\n",
       "      <td>46.09</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.17</td>\n",
       "      <td>28.60</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>10.90</td>\n",
       "      <td>16.56</td>\n",
       "      <td>10.90</td>\n",
       "      <td>16.56</td>\n",
       "      <td>25.65</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.55</td>\n",
       "      <td>5.66</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>59.13</td>\n",
       "      <td>45.18</td>\n",
       "      <td>59.13</td>\n",
       "      <td>45.18</td>\n",
       "      <td>77.59</td>\n",
       "      <td>18.47</td>\n",
       "      <td>0.31</td>\n",
       "      <td>13.95</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>39.49</td>\n",
       "      <td>74.15</td>\n",
       "      <td>39.49</td>\n",
       "      <td>74.15</td>\n",
       "      <td>84.36</td>\n",
       "      <td>10.21</td>\n",
       "      <td>0.14</td>\n",
       "      <td>34.65</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>11.55</td>\n",
       "      <td>29.80</td>\n",
       "      <td>11.55</td>\n",
       "      <td>29.80</td>\n",
       "      <td>37.91</td>\n",
       "      <td>8.11</td>\n",
       "      <td>0.27</td>\n",
       "      <td>18.25</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  cluster_p  \\\n",
       "9    HN120       1007     Docetaxel        201  Epothilone B          1   \n",
       "10   HN120       1007     Docetaxel       1010     Gefitinib          1   \n",
       "20   HN120        133   Doxorubicin       1012    Vorinostat          1   \n",
       "24   HN120        201  Epothilone B        302        PI-103          1   \n",
       "45   HN137       1007     Docetaxel        201  Epothilone B          1   \n",
       "\n",
       "    cluster_kill_A  cluster_kill_B  kill_A  kill_B  kill_C  improve  \\\n",
       "9            10.90           39.49   10.90   39.49   46.09     6.59   \n",
       "10           10.90           16.56   10.90   16.56   25.65     9.09   \n",
       "20           59.13           45.18   59.13   45.18   77.59    18.47   \n",
       "24           39.49           74.15   39.49   74.15   84.36    10.21   \n",
       "45           11.55           29.80   11.55   29.80   37.91     8.11   \n",
       "\n",
       "    improve_p  sum_kill_dif            Combi Name 1            Combi Name 2  \\\n",
       "9        0.17         28.60  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "10       0.55          5.66     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "20       0.31         13.95  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "24       0.14         34.65     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "45       0.27         18.25  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "\n",
       "                Combi Name  \n",
       "9   Docetaxel|Epothilone B  \n",
       "10     Docetaxel|Gefitinib  \n",
       "20  Doxorubicin|Vorinostat  \n",
       "24     Epothilone B|PI-103  \n",
       "45  Docetaxel|Epothilone B  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare for all lines and drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.402992Z",
     "start_time": "2020-11-17T09:54:25.382387Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "#pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.419981Z",
     "start_time": "2020-11-17T09:54:25.408840Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.470623Z",
     "start_time": "2020-11-17T09:54:25.425413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 6)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>72.39</td>\n",
       "      <td>74.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>77.52</td>\n",
       "      <td>67.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>81.74</td>\n",
       "      <td>64.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>84.66</td>\n",
       "      <td>73.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>51.99</td>\n",
       "      <td>63.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  72.39                   74.15  \n",
       "121                  77.52                   67.72  \n",
       "122                  81.74                   64.25  \n",
       "123                  84.66                   73.65  \n",
       "124                  51.99                   63.91  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:25.480191Z",
     "start_time": "2020-11-17T09:54:25.476210Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:26.313335Z",
     "start_time": "2020-11-17T09:54:25.485808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.68 (1.11e-06)\n",
      "Single drug [R-sq 45.53%]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "# plt.tight_layout()\n",
    "\n",
    "fig.savefig('../figure/Fig4E_single_drug_{}_cell.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:26.364764Z",
     "start_time": "2020-11-17T09:54:26.320958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>82.03</td>\n",
       "      <td>84.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>90.08</td>\n",
       "      <td>77.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>85.65</td>\n",
       "      <td>71.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>88.91</td>\n",
       "      <td>79.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>55.05</td>\n",
       "      <td>67.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  82.03                   84.36  \n",
       "66   HN137                  90.08                   77.34  \n",
       "67   HN148                  85.65                   71.44  \n",
       "68   HN159                  88.91                   79.36  \n",
       "69   HN160                  55.05                   67.08  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.173338Z",
     "start_time": "2020-11-17T09:54:26.371257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.53 (5.97e-03)\n",
      "Drug combination [R-sq 3.35%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "fig.savefig('../figure/Fig4_combi_drug_{}_cell.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.197320Z",
     "start_time": "2020-11-17T09:54:27.178619Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.219077Z",
     "start_time": "2020-11-17T09:54:27.202989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>patient</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combi Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>6.59</td>\n",
       "      <td>8.11</td>\n",
       "      <td>7.10</td>\n",
       "      <td>6.85</td>\n",
       "      <td>7.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>9.09</td>\n",
       "      <td>9.62</td>\n",
       "      <td>7.19</td>\n",
       "      <td>7.55</td>\n",
       "      <td>7.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>10.02</td>\n",
       "      <td>11.71</td>\n",
       "      <td>15.24</td>\n",
       "      <td>10.70</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>10.21</td>\n",
       "      <td>9.62</td>\n",
       "      <td>7.20</td>\n",
       "      <td>5.71</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>18.47</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.69</td>\n",
       "      <td>20.70</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "patient                 HN120  HN137  HN148  HN159  HN160\n",
       "Combi Name                                               \n",
       "Docetaxel|Epothilone B   6.59   8.11   7.10   6.85   7.97\n",
       "Docetaxel|Gefitinib      9.09   9.62   7.19   7.55   7.79\n",
       "Gefitinib|Epothilone B  10.02  11.71  15.24  10.70   7.38\n",
       "Epothilone B|PI-103     10.21   9.62   7.20   5.71   3.17\n",
       "Doxorubicin|Vorinostat  18.47  22.00  23.69  20.70  15.70"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.253967Z",
     "start_time": "2020-11-17T09:54:27.225273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>55.10</td>\n",
       "      <td>64.60</td>\n",
       "      <td>39.32</td>\n",
       "      <td>44.73</td>\n",
       "      <td>25.05</td>\n",
       "      <td>60.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>43.22</td>\n",
       "      <td>46.48</td>\n",
       "      <td>57.34</td>\n",
       "      <td>42.40</td>\n",
       "      <td>8.10</td>\n",
       "      <td>37.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>46.41</td>\n",
       "      <td>55.18</td>\n",
       "      <td>41.84</td>\n",
       "      <td>23.98</td>\n",
       "      <td>14.82</td>\n",
       "      <td>56.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>82.10</td>\n",
       "      <td>90.42</td>\n",
       "      <td>86.49</td>\n",
       "      <td>89.11</td>\n",
       "      <td>56.33</td>\n",
       "      <td>40.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>67.57</td>\n",
       "      <td>70.24</td>\n",
       "      <td>63.18</td>\n",
       "      <td>61.33</td>\n",
       "      <td>23.57</td>\n",
       "      <td>66.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160  HN182\n",
       "Drug                                                            \n",
       "Docetaxel|Epothilone B  55.10  64.60  39.32  44.73  25.05  60.92\n",
       "Docetaxel|Gefitinib     43.22  46.48  57.34  42.40   8.10  37.92\n",
       "Doxorubicin|Vorinostat  46.41  55.18  41.84  23.98  14.82  56.37\n",
       "Epothilone B|PI-103     82.10  90.42  86.49  89.11  56.33  40.31\n",
       "Gefitinib|Epothilone B  67.57  70.24  63.18  61.33  23.57  66.17"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').mean()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.299082Z",
     "start_time": "2020-11-17T09:54:27.264105Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "    best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').mean()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.836943Z",
     "start_time": "2020-11-17T09:54:27.305603Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter_cell.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.863675Z",
     "start_time": "2020-11-17T09:54:27.842647Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>10.81</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>2.80</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>20.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>7.37</td>\n",
       "      <td>18.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>10.23</td>\n",
       "      <td>15.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>5.43</td>\n",
       "      <td>11.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>13.98</td>\n",
       "      <td>10.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>6.09</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.19</td>\n",
       "      <td>10.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.87</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>12.90</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.92</td>\n",
       "      <td>7.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-6.17</td>\n",
       "      <td>7.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>6.73</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>4.75</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>4.39</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>-12.89</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>3.80</td>\n",
       "      <td>5.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "22  Doxorubicin|Vorinostat   HN148                      10.81   \n",
       "21  Doxorubicin|Vorinostat   HN137                       2.80   \n",
       "23  Doxorubicin|Vorinostat   HN159                      -1.82   \n",
       "20  Doxorubicin|Vorinostat   HN120                       7.37   \n",
       "24  Doxorubicin|Vorinostat   HN160                      -3.07   \n",
       "12  Gefitinib|Epothilone B   HN148                      10.23   \n",
       "11  Gefitinib|Epothilone B   HN137                       5.43   \n",
       "13  Gefitinib|Epothilone B   HN159                      13.98   \n",
       "15     Epothilone B|PI-103   HN120                       6.09   \n",
       "10  Gefitinib|Epothilone B   HN120                       9.19   \n",
       "6      Docetaxel|Gefitinib   HN137                       7.87   \n",
       "16     Epothilone B|PI-103   HN137                      12.90   \n",
       "5      Docetaxel|Gefitinib   HN120                       9.35   \n",
       "1   Docetaxel|Epothilone B   HN137                      -0.21   \n",
       "4   Docetaxel|Epothilone B   HN160                      -3.92   \n",
       "9      Docetaxel|Gefitinib   HN160                      -6.17   \n",
       "8      Docetaxel|Gefitinib   HN159                       6.73   \n",
       "14  Gefitinib|Epothilone B   HN160                      -5.40   \n",
       "17     Epothilone B|PI-103   HN148                       4.75   \n",
       "7      Docetaxel|Gefitinib   HN148                       4.39   \n",
       "2   Docetaxel|Epothilone B   HN148                     -12.89   \n",
       "3   Docetaxel|Epothilone B   HN159                      -2.62   \n",
       "0   Docetaxel|Epothilone B   HN120                      -3.28   \n",
       "18     Epothilone B|PI-103   HN159                       3.80   \n",
       "19     Epothilone B|PI-103   HN160                       4.34   \n",
       "\n",
       "    Predicted % death increase  \n",
       "22                       23.69  \n",
       "21                       22.00  \n",
       "23                       20.70  \n",
       "20                       18.47  \n",
       "24                       15.70  \n",
       "12                       15.24  \n",
       "11                       11.71  \n",
       "13                       10.70  \n",
       "15                       10.21  \n",
       "10                       10.02  \n",
       "6                         9.62  \n",
       "16                        9.62  \n",
       "5                         9.09  \n",
       "1                         8.11  \n",
       "4                         7.97  \n",
       "9                         7.79  \n",
       "8                         7.55  \n",
       "14                        7.38  \n",
       "17                        7.20  \n",
       "7                         7.19  \n",
       "2                         7.10  \n",
       "3                         6.85  \n",
       "0                         6.59  \n",
       "18                        5.71  \n",
       "19                        3.17  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.875560Z",
     "start_time": "2020-11-17T09:54:27.869117Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:27.885877Z",
     "start_time": "2020-11-17T09:54:27.881899Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:28.296706Z",
     "start_time": "2020-11-17T09:54:27.891666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=2.4635200565311153, pvalue=0.013758019786546642) Ttest_indResult(statistic=1.3142534995977282, pvalue=0.20171777155775344)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "# sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (stats.ranksums(x, y), stats.ttest_ind(x, y))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_cell.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:28.310367Z",
     "start_time": "2020-11-17T09:54:28.302347Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.46 (1.38e-02)\n"
     ]
    }
   ],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:28.325041Z",
     "start_time": "2020-11-17T09:54:28.315736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 14, 1: 11})"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:28.340948Z",
     "start_time": "2020-11-17T09:54:28.330535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.48, 0.6285714285714286, 0.13333333333333333)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T09:54:28.611493Z",
     "start_time": "2020-11-17T09:54:28.346318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6525163116740657\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
